WildfireRiskFinder

Laud, IN

Laud wildfire risk explained

Low
1stpercentile nationally

USFS's Wildfire Risk to Communities model puts Laud at the 1st national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 142 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Laud at the 1st percentile, close to its 1st-percentile risk score.

Laud's building exposure, zone by zone

142Total buildings
58.5%Direct exposure
0%Indirect exposure
41.6%Minimal exposure

142 buildings are counted in Laud, and 58.5% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 41.6% rated Minimal.

Where Laud ranks

Laud scores 1st nationally and 5th within Indiana — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Laud ranks 31,266 for wildfire risk (1 is highest) and 27,201 by building count (1 is largest). Within Indiana alone, it ranks 912 of 967 places by risk. See the full county-by-county picture for Indiana on its state page.

Shopping for coverage in Laud

Laud's low rating (1st percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Hardening a home in Laud

With 58.5% of Laud in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.

Where Laud's figures come from

Every one of the two percentiles behind Laud's 31,266-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Laud's dominant direct exposure actually means, with real examples from across the dataset.